Terrain elevation over forested areas is important for studies such as hydrological modeling and soil erosion. The spaceborne full-waveform LiDAR missions including Geoscience Laser Altimeter System (GLAS) and Global Ecosystem Dynamics Investigation (GEDI) provide freely available terrain elevation products indirectly and directly. However, the accuracies have seldom been evaluated in the same region. Here, we examined the terrain elevation accuracy and assessed the influence of terrain slope in forested areas using high-resolution airborne LiDAR data as a reference. The root mean square error (RMSE) of terrain elevation computed from all the data of GLAS and GEDI is 5.1 m and 8.4 m, respectively. Even though the footprint diameter of GEDI is much smaller than GLAS (25 m vs. 65 m), we still found a significant terrain effect with the increase of slope in GEDI. The RMSE of terrain elevation from GLAS is 3.4 m, 7.6 m, and 10.5 m when the slope ranges from 0° to 30° with an increment of 10°. The RMSE of terrain elevation from GEDI is 5.2 m, 8.8 m, 12.2 m, 14.1 m, and 16. 9 m when the slope ranges from 0° to 50° with an increment of 10°.

Land surface temperature plays a crucial role in ecosystem energy balance and material exchanges. Remote sensing is vital for investigating brightness temperature variations. The intricate canopy structure poses challenges, especially with strong directional anisotropy in brightness temperature, leading to assessment inaccuracies. The radiative transfer model provides valuable insights into how canopy structure influences directional brightness temperature (DBT). Traditional models, assuming a turbid medium or randomly distributed ideal geometry, exhibit notable errors. However, the PATH_RT model, incorporating path length distribution, shows commendable performance in the optical domain. To enhance applicability, we modify the PATH_RT model, successfully implementing path length distributions for simulating DBT in the thermal domain. Validation using abstract scenes, cross-validated with SAIL and FRT, and referencing DART, highlights significant improvement attributed to the efficacy of path length distribution.

Fractional vegetation cover (FVC) is of great significance to vegetation-related research, and FVC ground measurement is of irreplaceable importance. Among them, the digital photography method is more commonly used. The photograph is a perspective (central) projection that causes the trunk appear in the photograph, resulting in an error in the measurement of FVC. Based on the three-dimensional reconstruction, this study proposed a method to correct the influence of tree trunks in FVC ground-based measurement. The experimental results show that the absolute error due to the trunk effect ranged from 0.31% to 5.87% under the experimental conditions of this study, and the error can be significantly reduced to within 0.5% after correction. How to promote the quality and efficiency of 3D reconstruction and extraction of trunk points in this method is a research direction that needs to be further explored.

Wildfires are essential for the health and regeneration of forest and rangeland ecosystems. However, as climate conditions become hotter and drier, wildfires have grown more intense and destructive. Monitoring wildfires with satellite data is crucial for understanding their extent, severity, and environmental consequences. In this study, we developed a straightforward method to map medium and large burned areas at a monthly scale using FY-3D/MERSI-II observations. First, monthly clear-sky images were composited to provide enhanced distinguishability between burned and unburned areas. Then, a multi-criteria method was used to identify candidate burned areas. Next, image post-processing techniques were implemented to minimize false detections and reduce omission errors. Finally, dNBR (difference Normalized Burn Ratio) of the burned area and biomass carbon density data were combined to obtain an estimation of carbon emissions. This method offers a simple and efficient way to track and analyze burned areas without depending on the positions of active fires. We applied this method to map the 2023 Canada fires, and the results closely align with media reports, enhancing our method’s credibility and utility for wildfire monitoring and assessment.

In this paper, we develop the Slant Leaf Area Index based Method (SLAIM) to estimate the leaf area of individual trees from single-scan Terrestrial Laser Scanner (TLS) data by introducing the concept of Slant Leaf Area Index (SLAI). SLAI quantifies the amount of leaves along the view direction and can be retrieved at given view zeniths using gap probability. Subsequently, leaf area can be accumulated by SLAI across the whole crown. The innovative SLAIM offers several advantages. Firstly, it operates with single-scan point clouds, which are more accessible than multiple-scan data. Secondly, it effectively corrects the clumping effect resulting from non-uniform leaf distribution. Both simulated and field-measured TLS point clouds of trees are used to test the method. The results show that the error of SLAIM is less than 10% in most cases.

Normalized difference vegetation index (NDVI) is one of the most widely used vegetation indices (VIs) to retrieve vegetation parameters such as fractional vegetation cover (FVC) and leaf area index (LAI). Due to the bidirectional reflectance distribution function (BRDF) effect on the surface, NDVI is greatly affected by the Sun-view angle, leading to considerable uncertainty in the parameters derived from NDVI. The angle effect can be corrected using the BRDF model. Nevertheless, the majority of satellites are unable to collect sufficient multiangle data in near real time to retrieve the parameters of the BRDF model. In this study, we proposed a correction model for the Sun-view angle effect of NDVI (SVAC) that only needed single view-angle observation for implementation. The SVAC model was developed based on a published cosine correction model (CCM) that corrected NDVI’s Sun angle effect. The simulated data and MODerate-resolution Imaging Spectroradiometer (MODIS) product data were used in validation. The results showed that the SVAC model performed well for all simulated scenes, where the NDVI’s uncertainty originating from the Sun-view angle was reduced by over 70% and 30% for homogeneous and nonhomogeneous vegetation, respectively. The validation with real data at the VAlidation of Land European Remote Sensing Instrumentations (VALERI) sites and the ImagineS sites demonstrated a percentage decrease of root mean square error (RMSE) of approximately 30%. The SVAC model can effectively reduce the NDVI’s sensitivity to Sun-view angles with high applicability and simplicity and is expected to facilitate the acquisition of vegetation parameters using angular-independent NDVI.

Individual trees are fundamental to urban ecosystems as they play an important role in energy transfer, pollutant removal, and habitat formation. Leaf area (LA) is an important factor to quantify the effect of individual trees on urban ecosystems. Terrestrial laser scanners (TLSs) are widely recognized as the most accurate devices for tree structural measurements. However, they face challenges in estimating LA from LA index (LAI) for individual trees primarily due to arbitrary and confusing horizontal projection areas. Occlusion and clumping effects further hinder the objective and accurate LA measurements of individual trees. Therefore, we developed the slant leaf area index-based method (SLAIM) to estimate the LA of individual trees from single-scan TLS data by introducing the concept of slant leaf area index (SLAI). SLAI quantifies the amount of leaves along the view direction, and it can be retrieved at given view zeniths using gap probability. Subsequently, LA can be accumulated by SLAI across the whole crown. Tests with simulated and field-measured TLS point clouds demonstrate SLAIM’s accuracy, with the relative errors (REs) in LA below 10% in most cases. Stratified LA validation reveals an$R^{2}$exceeding 0.77 across all realistic crowns, along with a root-mean-square error (RMSE) under 2 m2. SLAIM’s advantages include compatibility with single-scan point clouds, effective correction of clumping effects, and consideration of variations in leaf projection coefficients at different zeniths. SLAIM proves more efficient and practical for actual LA measurements, showcasing its potential for advanced urban ecosystem research.

Leaf area index (LAI) is a critical indicator for modeling global biosphere–climate interactions. Accurately measuring forest LAI significantly affects the objectivity and accuracy of LAI remote sensing products. DHP is the most widely used instrument to measure forest LAI. However, it is challenging to measure forest LAI accurately using DHP at meter-scale as the wide viewing angle range of DHP is not appropriate for validation of high-resolution pixels. To address this issue, a geometric-based method was proposed to obtain the optimal view zenith range for DHP-based LAI field measurements at meter-scale in this study. Three virtual forest scenes were generated by LESS (LargE-Scale remote sensing data and image Simulation framework) and field measurements at Saihanba, in northern China was collected. The validation results indicate a good agreement between LAI estimation using proposed method and reference LAI datasets. The R2between the LESS-derived LAI and DHP-derived LAI are all greater than 0.9, and the RMSE are all less than 0.1 in simulated scenes. Meanwhile, the R2between the UAV lidar-derived LAI and DHP-derived LAI is 0.668 with RMSE of 0.376. In conclusion, this study holds potential in measuring forest LAI at meter-scale.

Leaf area index (LAI) field measurements based on digital hemispherical photography (DHP) and LAI-2200 instruments have been widely used for remote sensing validation in forestry. Both DHP and LAI-2200 utilize fish-eye lens to capture the largest footprint of a canopy with a wide range of view zenith angle (VZA). However, accurately measuring field LAI at high spatial resolution poses a significant challenge since the view scope of fish-eye sensor is much larger than the size of high spatial resolution pixel. Therefore, selecting appropriate VZA ranges is crucial to address this issue. In this letter, we propose an improved geometry-based method that considers the average tree height, crown depth, and high-resolution pixel size. To validate this method, we designed four simulated forest scenes with different crown shapes through the LargE-Scale Remote Sensing Data and Image Simulation Framework (LESS) model and conducted field measurements. The results indicate that our proposed method significantly enhances the accuracy of fisheye-based LAI field measurements at high spatial resolution compared to previous method, with an almost 70% reduction in RMSE. In addition, our method exhibits greater improvement in measuring forest LAI at high resolution with DHP (RMSE < 0.3) compared to LAI-2200 (RMSE < 0.5). In conclusion, our method holds great potential in accurately measuring fisheye-based forest LAI for remote sensing validation at high spatial resolution.

Changes in the sun zenith angle (SZA) alter the normalized difference vegetation index (NDVI) and introduce uncertainties into the estimation of vegetation biochemical and biophysical parameters. For the NDVI obtained from narrow swath width sensors, there is not a unified and easy-to-use approach to correct the sun-angle effect. In this study, the cosine correction model (CCM) was proposed to reduce the sun-angle effect on NDVI based on the path length (PL) of light calculated from the SZA without the need for multi-angle observations. The PL was found to be closely correlated to the simple ratio vegetation index (SR) and can mitigate the impact on the NDVI caused by SZA variations. The CCM performed well when correcting the sun-angle effect on NDVI for different types of data. After correction for the simulated data (e.g., the reference SZA of 10°), the coefficient of variation (CV) of the NDVI concerning SZA variations from 10° to 60° was reduced by 5.42%, and the root-mean-square error (RMSE) was reduced by 0.049. For the field-measured data, the CV of the NDVI under various SZAs was reduced by up to 5.55% after correction, and the maximum difference between the uncorrected and corrected NDVI was 0.099. The RMSE of corrected nadir NDVI from MODIS satellite data was reduced by 34.2% on average. The CCM, as an easily-implemented method, can attenuate the sun-angle effect on NDVI without relying on the BRDF products and hence has the potential to improve the accuracy of remote sensing monitoring of vegetation dynamics.

Clumping effect denotes the nonrandomness of foliage. It deviates from the random distribution assumption of Beer’s law which is usually applied to leaf area index (LAI) retrieval from large-footprint full-waveform light detection and ranging (LiDAR). Some studies correct for large gaps-induced between-crown clumping, yet ignore the within-crown clumping. The error of LAI caused by these clumping effects and the influence of the forest structure parameters on them have not been quantitatively studied. This study quantified the between-crown, within-crown, and total clumping indices through a theoretical derivation, clarifying the mechanism of clumping; we used airborne LiDAR point clouds data in 11 290 footprints (diameter = 25 m) to estimate these indices in real forests. We found that: 1) the underestimation of LAI caused by directly applying Beer’s law could be up to 93%, and it decreases with fractional crown coverage but increases with crown length and leaf area density; 2) the method of correcting between-crown clumping improves LAI retrieval for cylindrical canopies effectively; however, 3) considerable underestimation (up to 58%) exists if we neglect the within-crown clumping for other canopies, which has not been realized before; and 4) both the between-crown and the within-crown clumping can be the dominant contributor, and the within-crown clumping was greater than the between-crown clumping in 47% of the studied footprints. In the two physically based LAI retrieval methods, Beer’s law has been commonly used due to its simplicity. Pathways to improve future LAI retrieval would be instrument improvement to capture the between-crown gaps and method study to correct the within-crown clumping further.

Remote sensing estimation based on the dimidiate pixel model (DPM) using vegetation indices (VIs) is a common approach for mapping fractional vegetation cover (FVC). The major drawback of DPM is that it does not consider real endmember conditions and multiple scattering between soil and vegetation. An analysis of FVC uncertainties caused by these model deficiencies is still lacking. Here, we first calculated the FVC theoretical uncertainty caused by reflectance uncertainties based on the law of prapagation of uncertainty (LPU). Then, we tested the performance of DPM using six VIs over 3-D forest scenes. We simulated both Aqua-MODIS and Landsat-OLI surface reflectance (SR) at their corresponding spatial resolutions and spectral response functions (SRFs) using a well-validated 3-D radiative transfer (RT) model which helps to separate the model and input uncertainties. We found that ratio vegetation index (RVI)- and enhanced vegetation index (EVI)-based models were most affected by sensors, followed by the normalized difference vegetation index (NDVI)-, enhanced vegetation index 2 (EVI2)-, renormalized difference vegetation index (RDVI)-, and difference vegetation index (DVI)-based models. Without considering SR uncertainties, the DVI-based model performed best (FVC absolute difference < 0.1); however, the commonly used NDVI model reached a maximum difference of 0.35. At the same time, input uncertainty increased the uncertainty of FVC retrieval. We noticed that the increase of solar zenith angle (SZA) resulted in a clear increase of retrieved FVC under the uniform distribution, which can be explained by the increased shadow proportion. Besides, model accuracy was dominated by the purity of soil (vegetation) endmember in low (high) vegetation cover area. This study provides a reference for the selection of the optimal VI for FVC retrieval based on the DPM.

Bidirectional reflectance distribution function (BRDF) models are used to correct surface bidirectional effects and estimate land surface albedo. Many operational BRDF/albedo algorithms adopt a Roujean linear kernel-driven BRDF (RLKB) model because of its simple form and good performance in fitting multidirectional surface reflectance values. However, this model does not explicitly consider topographic effects, resulting in errors when applied over rugged terrain. To address this issue, we proposed a hybrid algorithm suitable for both flat and rugged terrain, called topographical kernel-driven (Topo-KD). First, we constructed a linear kernel-driven BRDF model considering terrain (LKB_T) which describes the topographic effects with a mountain radiative transfer (MRT) model. Then, the Topo-KD algorithm adaptively selects the most suitable model (RLKB or LKB_T) according to the terrain conditions and fitting residuals. The performances of Topo-KD and RLKB using the RossThick–LiSparseReciprocal (RTLSR) kernel are compared using simulated data sets and moderate-resolution imaging spectroradiometer (MODIS) observations. The results show that the BRDF of the pixel is affected by topography. But the RTLSR model does not specifically account for it, resulting in larger biases over rugged terrain than the Topo-KD algorithm in both the red and near-infrared (NIR) bands. The experiment using MODIS data sets demonstrates that the Topo-KD algorithm reduces fitting residuals in the red and NIR bands by 21.5% and 27.4% compared with the RTLSR model. These results indicate that the Topo-KD algorithm can be a better choice for retrieving land surface parameters and describing the radiative transfer process in mountainous areas.

Occlusion effect, an inherent problem of terrestrial laser scanning (TLS) measurements, limits the potential of TLS data in tree attribute estimation. Multiple scans seek to mitigate this effect to provide enhanced scan completeness. However, the numbers and locations of the scans (i.e., the scan design) are usually determined via a subjective assessment of the tree density, spatial patterns of trees, and attributes to be derived. These could cause suboptimal scan completeness and limit tree attribute estimation. This study proposed an iterative-mode scan design to minimize the occlusion effect. First, we introduced a PoTo index based on visibility analysis to evaluate how many trees can be scanned from a location and to select effective candidates for the optimal TLS location. Second, we introduced a cumulative degree of ring closure (CDRC) to quantify the scan completeness for each candidate and determine the optimal TLS location. The TLS data sets of virtual forests with field-measured and synthetic plot parameter settings were simulated according to iterative- and regular-mode designs by using a Heidelberg light detection and ranging (LiDAR) Operations Simulator (HELIOS). The results demonstrated that an iterative-mode design can improve the scan completeness of trees compared to the regular-mode design. The tree attribute (diameter at breast height (DBH), tree height, stem curve, and crown volume) estimates of the iterative-mode design were less erroneous than those of the regular-mode design (e.g., the root-mean-square error (RMSE) could decrease the stem curve estimation by 38% and the crown volume estimation by 15%). This study suggests that the iterative-mode design can obtain an improved quality of the TLS data, especially for dense stands.

Estimation of downward shortwave radiation (DSR) is of great importance in global energy budget and climatic modeling. Although various algorithms have been proposed, effective validation methods are absent for rugged terrains due to the lack of rigorous methodology and reliable field measurements. We propose a two-step validation method for rugged terrains based on computer simulations. The first step is to perform point-to-point validation at local scale. Time-series measurements were applied to evaluate a three-dimensional (3-D) radiative transfer model. The second step is to validate the DSR at pixel-scale. A semiempirical model was built up to interpolate and upscale the DSR. Key terrain parameters were weighted by empirical coefficients retrieved from ground-based observations. The optimum number and locations of ground stations were designed by the 3-D radiative transfer model and Monte Carlo method. Four ground stations were selected to upscale the ground-based observations. Additional three ground stations were set up to validate the interpolated results. The upscaled DSR was finally applied to validate the satellite products provided by MODIS and Himawari-8. The results showed that the modeled and observed DSR exhibited good consistency at point scale with correlation coefficients exceeding 0.995. The average error was around 20 W/m2for the interpolated DSR and 10 W/m2for the upscaled DSR in theory. The accuracies of the satellite products were acceptable at most times, with correlation coefficients exceeding 0.94. From an operational point of view, our method has an advantage of using small amount of ground stations to upscale DSR with relatively high accuracy over rugged terrains.

The clumping index (CI) describing the spatial pattern and distribution of leaves in vegetation canopy, is a key parameter for accurate retrieval of leaf area index (LAI). In this study, we focused on the individual tree-level CI (i.e., total CI) and decomposed it into the crown shape-related CI and crown internal structure-related CI. Using the simulation datasets from OnyxTree realistic structure software, we calculated all the above-mentioned CI for tree crowns with LAI values from three tree species. We also analyzed the angular effect of these CI. Results showed that the crown shape-related CI generally dominated the clumping effect of leaves although it varied with zenith angle. Nevertheless, the internal structure-related CI was hard to be neglected, especially for the case of Platanus with high LAI. Our results also demonstrated that the role of each kind of CI depended on the tree species.

The Tarim River Basin is the largest and the most arid basin in northwest China, where has an extreme hot and dry climate. The ecological environment of the river basin is especially fragile and vegetation dynamics are very sensitive there. In order to analyze the vegetation restoration effectiveness and provide basic data for ecological research of the river basin, here mapping 30m fractional vegetation cover dynamics in spring, summer and winter from 2008 to 2018 in virtue of cloud computing services of Landsat data provided by the Google Earth Engine (GEE). The average vegetation coverage in summer tended to improve in the Tarim river basin during the past 11 years (R2 = 0.8744) and the annual average growth rate was 0.35%. The highest vegetation cover occurred in 2017 and increased 58.83% compared to the lowest vegetation coverage in 2009.

Terrain affects surface solar radiation (SSR) of mountainous area greatly. However, reliable observational data and methods are absent in mountainous areas. The stations located in these areas, typically built on flat places equipped with horizontal radiometers, are hard to capture the topographic effects. We proposed a tilted SSR observation group for mountainous areas based on ground stations. In 2015 and 2016, the method was tested in Chengde, China. Five ground stations were built on hilltop, valley, and three slopes to measure SSR. The radiometers on slopes and hilltop were set up parallel to the ground surfaces, while the radiometer in the valley was set horizontally to compare with the tilted observation method. A topographic radiation model along with a 12.5m digital elevation model (DEM) data was used to simulate the downward SSR and compare with observations. The result showed good consistency with the observations on slopes with R2values as high as 0.99, but relatively big deviations were found at the hilltop and valley stations, caused by the slope calculation errors and unsuitable observational method. The results demonstrate the fact of that the topographic radiation model should be validated using proposed method with high accuracy DEM.

Land-surface bidirectional reflectance distribution function (BRDF) models are used for the description of surface bidirectional effects and the estimation of surface albedo. The semi-empirical linear kernel-driven BRDF model is one of them which has been adopted by the moderate resolution imaging spectroradiometer (MODIS) operational BRDF/Albedo algorithm, due to its briefness and well-fitting ability. However, this model does not consider the topography factors, and will lead to errors over rugged terrains. However, researches seldom analyze the models' uncertainties caused by rugged terrains quantitatively, as it is difficult to directly validate models over mountain areas at coarse resolution. This letter proposes a forward topographic BRDF simulation method by combining a canopy radiative transfer model (SAILH) and a mountain radiative transfer (MRT) model to investigate the uncertainty and sensitivity of the kernel-driven model over mountain areas theoretically. Results show that the topographic effects can cause over 20% uncertainties on both red and NIR bands. Topography leads to the asymmetry of BRDF distributions on azimuth, which cannot be captured by kernel-driven model at 1km scale. Both DEM types and observation situations influence the retrieval accuracy significantly. Therefore, this work is meaningful to study the optimal inversion scale and observation requirements depending on the topography.

Accurate and efficient measurement of leaf angle distribution (LAD) is important for characterizing canopy structures and understanding solar radiation regimes within the plant canopy. The main challenge for obtaining LAD is measuring the orientations of individual leaves rapidly and accurately in complex field conditions. In this letter, we propose an efficient and low-cost approach to estimate both leaf zenith and azimuth angles from smartphone photographs by using a structure from motion (SfM) point cloud and pyramid convolutional neural network (PCNN)-based leaf detection. This SfM-PCNN method first detects individual leaves from 2-D photographs by delineating leaf boundaries, while minimizing the influences of interior leaf textures. The segmented image with leaf annotations is then used to partition the 3-D SfM point cloud into leaf clusters, each of which is fit by a plane to calculate the leaf orientation. The method was validated with manual measurements for five plant species with different leaf sizes, leaf shapes, and leaf textures. The accuracy is satisfactory for a leaf-to-leaf comparison over a Euonymus japonicus Thunb. with R-squared values of 0.84 (RMSE = 6.27°) and 0.97 (RMSE = 12.61°) for zenith and azimuth angle estimations, respectively. The method allows researchers to efficiently acquire LADs of different plants with low cost yet high accuracy.

Rugged terrain, as a high percent of the Earth's terrestrial surface, can cause the directionality of the surface thermal radiation, and affect the retrieved land surface temperature (LST) and longwave radiation (SLR) from satellite measurements due to the limited instantaneous field of view and observation angles. New directional brightness temperature (DBT) and equivalent brightness temperature (EBT) models were established considering terrain effects. The biases between them were also analyzed based on a simulated scene using the Advanced Spacebome Thermal Emission and Reflection Radiometer (ASTER) LST, emissivity and topographic data. The results show that BTs at the valley and peak points are clearly anisotropic, while this directionality at the cropland point is not obvious. The DBT shows hotspot effects which is closely related to the solar position. The range of DBTs can reach up to about 9 K in the valley point and the standard deviation of this difference in all view directions is 1.05 K. Thus, it can be concluded that it is hard to meet the requirement of retrieval accuracy of LST or SLR over rugged terrain if ignoring the three-dimensional structure of mountainous region and its angular thermal radiation.

The airborne laser scanner (ALS) provides great potential for mapping the leaf area index (LAI) at the landscape scale using grid cell statistics, while its application is restricted by the lack of clumping information, which has been an unsolved issue highlighted for a long time. ALS generally provides an effective LAI because its footprint is too large to capture small gaps to apply traditional ground-based clumping correction methods. Here, we present a grid cell method based on path length distribution model to calculate the clumping-corrected LAI using ALS data without the requirement of additional field measurements. We separated the within- and between-crown areas to consider between-crown clumping, and used the path length distribution as estimated by local canopy height distribution to consider 3-D foliage profile and within-crown clumping. The path length distribution model takes advantage of the 3-D information rather than the gap size distribution, thus avoiding the limitation of large ALS footprint. With the 0.4-m-footprint ALS data, the results are generally promising and a multilevel clumping analysis is consistent with landscape flown. The ALS LAIs of different resolutions are consistent, with a difference of less than 5% from 5- to 250-m resolutions. Due to its consistency and simple configuration, the method provides an opportunity to map the clumping-corrected LAI operationally and strengthens the ability of airborne lidar to monitor vegetation change and validate the satellite product. This grid cell method based on path length distribution is worth further testing and application using more recent laser technology.

Estimation of daily downward shortwave radiation (DSR) is of great importance in global energy budget and climatic modeling. The combination of satellite-based instantaneous measurements and temporal extrapolation models is the most feasible way to capture daily radiation variations at large scales. However, previous studies did not pay enough attention to topographic effects and simple temporal extrapolation methods were applied directly to rugged terrains which cover a large amount of the land surface. This paper, divided into two parts, aims at analyzing the topographic uncertainties of existing models and proposing a better method based on a mountain radiative transfer (MRT) model to calculate daily DSR. As the first part, this paper analyze the spatiotemporal variations of DSR influenced by topographic effects and checks the applicability of three temporal extrapolation methods on cloud-free days. Considering that clouds also have a strong influence on solar radiation, cloud-free days are chosen for targeted analysis of topographic effects on DSR. Three indices, the coefficient of variation, entropy-based dispersion coefficient (CH), and sill of semivariogram, are put forward to give a quantitative description of spatial heterogeneity. Our results show that the topography can dramatically strengthen the spatial heterogeneity of DSR. The index, CH, has an advantage for quantifying spatial heterogeneity as it offers a tradeoff between accuracy and efficiency. Spatial heterogeneity distorts the daily variation of DSR. Application of extrapolation methods in rugged terrains leads to overestimation of daily average DSR up to 60 W/m2 and a maximum 200 W/m2 error of instantaneous DSR on cloud-free days. This paper makes a quantitative analysis of topographic effects under different spatiotemporal conditions, which lays the foundation for developing a new extrapolation method.

Mixed pixels have a significant impact on the accurate estimation of Fractional Vegetation Cover (FVC) using digital photos acquired by Unmanned Aerial Vehicle (UAV). A single threshold is inadequate for the separation of vegetation and background when images contain numerous mixed pixels. We propose a spectral unmixing method to measure FVC with UAV-acquired digital images. In this method, the spectral mean values of vegetation and background are obtained as a priori spectral knowledge from the photos taken at a very low flight altitude around 5 meters above ground level (AGL). Two thresholds with high confidence level derived from the a priori knowledge are determined to select pure vegetation and background pixels from the photos taken at high flight altitudes ranging from dozens to hundreds of meters AGL. For the mixed pixels, endmember spectra are undertook by mean values of those two pure components. Images with different aggregation levels were generated from a 10 meters AGL image. A comparison with four commonly used methods indicated that our method could robustly characterize the FVC in a good agreement with the ground truth, and the accuracy of FVC estimates over corn crops was around 0.01 in terms of root mean square error (RMSE) value. All aggregated images produced stable FVC estimates and the corresponding standard deviation (STD) was around 0.01 with relative average deviation (RAD) being less than 0.15.

The systemic validation works were carried out at a watershed scale based on the ground-based observation data of the Heihe Watershed Allied Telemetry Experimental Research (HiWATER). Three validation strategies, scaling-up, spatial representation analysis, footprint analysis were used based on different data acquirement techniques. Some studies were performed and four types of remote sensing products were validated. This paper makes a general introduction on the validation results based on these systematic validation activities, which aims to support the integrated study of the water-ecosystem-economy in the Heihe River Basin.

The environment of Greater Mekong Sub-region (GMS) was highly payed attention to its economic development. Remote sensing technology was a useful tool for global and regional environment monitor. The fractional vegetation cover (FVC) with 30m spatial resolution for GMS was extracted from the HJ-1/CCD data in this study. The vegetation covers were highly for the entire GMS, and the spatial differences were influenced by vegetation types. Besides, FVC product with high temporal resolution (5 days) and 1km spatial resolution were used to analyze the vegetation damages by typhoon Haiyan from 2edto 10thNov., 2013 based on a change detection method. The damage extents of forest by typhoon were much seriously than cropland and grassland for GMS memberships, especially for Vietnam and China (Guangxi). The vegetation damages varied from -50% to 10% in the 300 km suffer areas on the typhoon Haiyan pathway.

Landscape heterogeneity is a common natural phenomenon but is seldom considered in current radiative transfer (RT) models for predicting the surface reflectance. This paper developed an analytical RT model for heterogeneous Agro-Forestry scenarios (RTAF) by dividing the scenario into nonboundary regions (NRs) and boundary regions (BRs). The scattering contribution of the NRs can be estimated from the scattering-by-arbitrarily-inclined-leaves-with-the-hot-spot-effect model as homogeneous canopies, whereas that of the BRs is calculated based on the bidirectional gap probability by considering the interactions and mutual shadowing effects among different patches. The multiangular airborne observations and discrete-anisotropic-RT model simulations were used to validate and evaluate the RTAF model over an agro-forestry scenario in the Heihe River Basin, China. The results suggest that the RTAF model can accurately simulate the hemispherical-directional reflectance factors (HDRFs) of the heterogeneous scenarios in the red and near-infrared (NIR) bands. The boundary effect can significantly influence the angular distribution of the HDRFs and consequently enlarge the HDRF variations between the backward and forward directions. Compared with the widely used dominant cover type (DCT) and spectral linear mixture (SLM) models, the RTAF model reduced the maximum relative error from 25.7% (SLM) and 23.0% (DCT) to 9.8% in the red band and from 19.6% (DCT) and 13.7% (SLM) to 8.7% in the NIR band. The RTAF model provides a promising way to improve the retrieval of biophysical parameters (e.g., leaf area index) from remote sensing data over heterogeneous agro-forestry scenarios.

Shortwave (SW) radiative flux (usually within 0.3∼3μm) is the dominant energy source of our planet, which drives the climate as well as the matter and energy cycle of the Earth system. It is an indispensable component of surface total energy balance. Considering the importance of SW radiation, during the past decades, more and more studies have conducted for estimating surface SW radiation using satellite-based data, such as MODIS, CERES, GOES etc. Although great effort has been made, most researches neglect the topographic effect and mainly focus on the retrieval of SW radiation over ideal horizontal surfaces for both instantaneous and time-integrated radiation. For this point, we propose a topographic SW radiation model based on the existing studies. Based on this, the SW radiative flux components are derived from MODIS data by fully accounting for the surface topographic effect. The results show that the errors induced in the retrieved daily SW radiation can reach up to 400W/m2at 1km scale. For instantaneous radiation, the uncertainties of derived SW radiation can reach up to 300W/m2even at 5km scale due to topographic effect. The findings of this paper prove the importance of topographic modeling of surface radiation over rugged terrain.

Thermal anomaly appears to be a significant precursor of some strong earthquakes. In this study, time series of 8-day MODIS Land Surface Temperature (LST) product are processed and analyzed to locate possible anomalies prior to the Songyuan earthquake (22 November 2013, Jilin Province). In order to reduce the seasonal or annual effects from the LST variations, also to avoid the rainy and cloudy weather in this area, a background data of 8-day LST are derived using averaging MOD11A2 products from 2001 to 2012. Then the 8-day LST data from September 2013 to January 2014 were differenced using the above background. RX anomaly detection algorithm was then used to detect the anomalies. It is found that day-time LST sequence detect some anomalies near the epi-center region, while through the night-time LST images, river change across seasons show obvious anomalies. The study indicates that LST change detection is somewhat effective in this area for earthquake precursor study.

This paper aimed at the case of nonisothermal pixels and proposed a daytime temperature-independent spectral indices (TISI) method to retrieve directional emissivity and effective temperature from daytime multiangular observed images in both middle and thermal infrared (MIR and TIR) channels by combining the kernel-driven bidirectional reflectance distribution function (BRDF) model and the TISI method. Four groups of angular observations and two groups of MIR and TIR channels with narrow and broad bandwidths were used to investigate the influence of angular observations and bandwidth on the retrieval accuracy. Model sensitivity analysis indicated that the new method can generally obtain directional emissivity and temperature with an error less than 0.015 and 1.5 K if the noise included in the measured directional brightness temperature (DBT) and atmospheric data was no more than 1.0 K and 10%, respectively. The analysis also indicated that 1) large-angle intervals among the angular observations and a larger viewing zenith angle, with respect to nadir direction, can improve the retrieval accuracy because those angle conditions can result in significant difference for components' fractions and DBT under different viewing directions; 2) narrow channels can produce better results than broad channels. The new method was finally applied to a multiangular MIR and TIR data set acquired by an airborne system, and a modified kernel-driven BRDF model was used for angular normalization to the surface temperature for the first time. The difference of the retrieved emissivity and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) emissivity was found to be approximately 0.012 in the study area.

Fractional Vegetation Cover (FVC) is a very important vegetation structural parameter. The coarse-resolution remote sensing images can be obtained easily and widely used. If the accuracy of FVC derived from coarse-resolution data is promoted, it will bring great convenience for application field. From this point of view, this paper aims to use the high-resolution data to validate the precision of FVC, which is calculated by the coarse-resolution data. We chose the research region in Heihe basin, China as the experiment area. The validation data is generated by fitting the field measured data and Advanced Spaceborne Thermal Emission and Reflection (ASTER) vegetation index - higher resolution remote sensed data. Finally, this paper compares the results and takes analysis.

Terrestrial LiDAR Scanning(TLS) technology can quickly acquire three-dimensional information of forest canopy with high precision. As a new technique of data collection, it has been gradually applied to characterize structural attributes such as plant area densities. This paper presented a ray-tracing method to simulate laser intersection with a single tree and retrieves the plant area index based on gap-fraction model. The simulation model, based on ray-tracing method, was highly dependent on the sensor configuration and the spatial characteristics of the tree examined. Plant area index was retrieved by the gap-fraction model using the simulated point clouds. Given the significant cost and complexity of LiDAR data acquisition, it was necessary to identify the operational parameters to maximize the benefit. Therefore, the factors that might affect the simulation and inversion procedures are discussed extensively. Results showed that the simulation model was capable of predicting what survey configuration would be optimal and facilitating inversion algorithm development.

The ground-measured emissivity is always affected by many kinds of noises, which lead the retrieval accuracy to be out of expectation. This paper investigates the influence of three major noises (formula simplification, surface temperature measurement, and temperature emissivity separation algorithm) on the spectral emissivity by using simulation data based on radiative transfer model and field measured data from portable 102F infrared spectrometer. The findings of this paper can provide some suggestions for the further emissivity measurement.

Accurate spectral calibration of the in-flight sensors is crucial for processing and exploration of remotely sensed data. This paper developed a strategy to make spectral recalibration (i.e., spectral response function, central wavelength, and bandwidth) for in-flight broadband sensor using a device-responsivity-decomposition model with a priori knowledge and an optimization algorithm. Sensitivity analysis indicates that an accurate result requires the targets to be observed under a dry and clear atmospheric condition (column water vapor2and visibility > 23 km) and no more than 5% error is included in the measured data. The new strategy was used to retrieve the spectral parameters along with radiometric calibration coefficients for a multichannel camera onboard an unmanned aerial vehicle from simultaneously remotely sensed and ground measured data sets over 19 (15 color-scaled and four gray-scaled) man-made surface targets, and the retrieved results were validated with a similar data set over another four man-made targets. It demonstrated that the camera's spectral parameters were accurately retrieved and an error less than 3.5 W/m2/μm/sr was brought to the channel radiance.

Fractional vegetation cover (FVC) is a major biophysical parameter in earth surface system. In this paper, FVC is retrieved with a simple linear model between FVC and Normalized Difference Vegetation Index (NDVI). However, the parameters NDVI∞and NDVI0, corresponding to the values of NDVI for bare soil and full vegetation covered surface, used in the simple model are estimated with a vegetation phenology model using time series MODIS NDVI data. The results of the estimated FVC with our proposed method in the study area have been showed in the results section, which is compared with the FVC estimated with a single date MODIS NDVI data. Validation has also been proved that the retrieved FVC has a good agreement with the ground-measured truth FVC.

Digital photography is now the most widely used method to obtain the Fractional Vegetation Cover (FVC) in field measurements. Its accuracy is affected by shooting conditions and classification methods of digital images. In this paper, we chose summer maize as the study plant, used computer simulation method to control the shooting conditions strictly and generate simulated scene. Then a physically based ray-tracing (PBRT) algorithm was used to render the scene to obtain simulated images under different shooting conditions. Supervised classification and CIE L*a*b* color space threshold method were used to extract FVC values from the simulated images. Comparing the extracted FVC values with the scene's true FVC value, we evaluated the FVC accuracy of different shooting conditions and classification methods. The results can act as a guidance of digital photography to obtain the FVC.

Gap fraction is a very important parameter to the indirect estimation of the true Leaf Area Index. In this paper, we combined the multispectral digital imageries (RGB color imagery and Near-Infrared imagery), which were obtained from a new device called Multispectral Canopy Imager (MCI), to estimate gap fraction. A new method incorporated with CIE L*a*b* color space has also been proposed to segment the multispectral digital imagery. The preliminary results of the estimated gap fraction have been showed in the conclusions section and been proved to be very well.

Net Surface Radiative Flux is the key parameter for global change studies. In this study, two models designed to directly estimate net surface radiative fluxes over horizontal surfaces are developed based on artificial neural network (ANN).These models not only avoid the error propagation involved in the existing algorithms, but also provide the necessary data for estimating fluxes over rugged terrain. The validation results show that the maximum root mean square error (RMSE) of the ANN models is less than 45W/m2and 25 W/m2for net shortwave and longwave fluxes, respectively. By coupling the outputs of ANN models, the shortwave and longwave topographic radiative models are subsequently proposed to derive the net surface fluxes over rugged terrain. The results indicate that great errors can be detected if the topographic effect is ignored over rugged area, especially for net shortwave radiative fluxes.

Fractional vegetation cover (FVC) is widely relevant for land surface process. In this paper, an algorithm is addressed on FVC retrieval, with the combination of MODIS and Huan Jing satellite (HJ), which is a newly launched constellation by China. In the developed model, we considered angular effect and utilized spatial and temporal information to a great extent. MODIS and HJ surface reflectance products provide data supply for the algorithm and play cooperative roles. A vegetation growth model was introduced to constrain the uncertainty of HJ data in a temporal scale. The uncertainty of using this algorithm was assessed by error propagation theory and field experiments. Retrieved FVC became more reasonable after consideration of the correlation among time series observations and the introduction of more observational data. A priori information is necessary to constrain the inversion process.

For intelligent planning of the land consolidation, the planning efficiency and the visualization are researched. A method is explored that knowledge of land consolidation planning is represented by object oriental technique and that the planning rules are derived by inference, and then, the rule base is built and correlated with 3D planning element model library. Try to use the rules to conduct the arrangement of the planning elements in 3D scene in order to improve the visualization, normalization and efficiency of the land consolidation planning.

Continuous LAI values are very important in crop growth monitoring, however, all of the remotely sensed LAI products are limited by the temporal and spatial resolution. High spatial resolution is good for crop monitoring but with very poor temporal sampling. The popular MODIS 8 day LAI product is still not sufficient for crop monitoring. An empirical crop growth model was developed based on two years' ground truth. It was then coupled with SAILH model to retrieve the continuous LAI day by day. A rolling inversion strategy was proposed further to minimize the random noise in the observations. The models coupled inversion was tested by simulation inversion. Results show significant improvements of the new inversion method.

Accurate radiometric and spectral calibrations of hyperspectral remote sensing instruments are essential for optimum data processing and exploitation. Two improved methods for the refinement of the spectral calibration of air- and spaceborne imaging spectrometers are presented in this paper. Both spectral channel position and width can be retrieved by modeling the atmospheric absorption features around 760, 940, 1140, and 2060 nm without making use of external atmospheric or surface parameters. A sensitivity analysis based on synthetic data demonstrated that, for each of the two methods, the root-mean-square errors to be expected were less than 0.18 nm for the retrieval of channel wavelength center and less than 0.8 nm for channel full-width at half-maximum. The application of the proposed methods to a real Hyperion data set showed quite-similar cross-track variations in the spectral calibration for the two methods, although relatively large differences in magnitude were found near the 940- and 1140-nm H2O absorption features. The significant improvement of the reflectance spectra derived after the refinement of the instrument spectral calibration confirms the good performance of the proposed methods.

The inversion of physical models in remote sensing is difficult due to its ill-posed essence. Though scientists have been realizing that the inversion result is much concerned with sensitivities of parameters, how to define the sensitivity of a parameter in inversion is still under discussion. In this paper, an "S Index" is proposed to derive S Ratio, a ratio of one input parameter's S Index to the sum of all other S Indices, as a useful sensitivity criterion. Moreover, we analyzed S index and S Ratio based on the information transfer theory. It is shown that S Ratio is related with information distribution ratio in inversion. The value of S Ratio may vary with different ground covers, soil types, moisture, geometries and bands. We took SAIL model as an example to illustrate the use of S Ratio under several typical scenes. Multi-angular datasets were generated for these scenes and further been used to retrieve 7 parameters of the model. The results suggest that the inversion accuracy is strongly correlated to S Ratio. Another two sensitivity indices are also demonstrated as a comparison. As a result, we could use it to estimate the sensitivity of parameters in a certain inversion step and which type of datasets is better for inversion under various cases. Such a priori information could be important before data selection

In this study, a thermal model-based algorithm has been developed. This linearized algorithm can invert land surface component temperatures in an ATSR pixel. The proportion of each component in an ATSR pixel is gotten using matched ASTER data. Then, assumed that the radiation of an ATSR pixel is the sum of the radiation from several components, we get a simple linear thermal model. Atmospheric effects on ATSR thermal data are removed using a split-window algorithm. Before inversion, the sensitivity of parameters is analyzed using Uncertainty and Sensitivity Matrix (USM). During the inversion process, a Multi-stage Sample-direction Dependent Target-decisions (MSDT) strategy is taken, that is, the most sensitive and uncertain parameters are inverted first by fixing some less sensitive parameters at their prior values. Compared with the general direct inversion method, MSDT strategy can get a more robust result. With other prior knowledge, this algorithm can invert soil and vegetation temperature.

Various remote sensing sensor observe the Earth's surface from coarse spatial resolution to fine spatial resolution. We may get different results from remote sensing images captured at different resolution due to scale effects. On the other hand, vegetation cover is an important parameter in many environmental models. It often affects the model results greatly. So, it is very important to understand the scaling problem of vegetation in remote sensing. This article presents a method to describe the vegetation cover distributions at different scales based on Bayesian Inference techniques. The histograms of vegetation cover show changing shapes with various spatial resolutions, they are very similar to Beta distributions with different parameters. On the other hand, geography spatial distribution probability can be expressed with binominal distribution or negative binominal distribution. Then, given a binominal or negative binominal distribution as likelihood, Beta distribution as a priori, we can get posterior distribution using conjugate prior theory. Such a posterior distribution can be used to predict the histograms of vegetation cover at different scales. Parameters used by this method can be calculated using mean and variance of vegetation cover at various scales. MODIS, Amtis and TM images are used to validate the method. The result shows that if vegetation is scattered, the binominal distribution may be used as the likelihood, on the contrary, a negative binominal distribution is much better. Because determining the spatial distribution is difficult, we combine the two distributions by adding the weight in this paper, and get better result.